Learn about LLM

Attention Head Wars: Why Your AI Misclassifies

Why do LLMs still misclassify data even with many examples?
Because attention heads compete.

What does this episode explain?
In the final seasonally-themed Floofies episode on attention, we break down attention head mechanics and why they can derail classification tasks.

What happens during classification?
Different attention heads encode different signals at once—word position, semantic relationships, and pattern matching against prior context.

Do attention heads work together?
No. They compete, and position-focused heads often dominate.

Why doesn’t adding more examples fix the issue?
More examples don’t help if they don’t resolve head competition.

What actually works?
Contrasting examples, explicit rules, and clearly defined exclusions.

  • AI, AttentionHeads, Context engineering, DataClassification

[00:00:00.000 --> 00:00:03.680] Good data analysis relies on good data classification.
[00:00:03.680 --> 00:00:07.840] But how do you trust your AI to apply classification rules reliably?
[00:00:07.840 --> 00:00:10.880] Today we're talking about attention heads,
[00:00:10.880 --> 00:00:14.240] the AI components that decide which words to focus on.
[00:00:14.240 --> 00:00:16.640] They learn your rules literally.
[00:00:16.640 --> 00:00:20.320] Incomplete rules lead to misclassified results.
[00:00:20.320 --> 00:00:24.880] But first, if you want AI that understands context,
[00:00:24.880 --> 00:00:28.640] visit ask-y.ai and try Prism.
[00:00:29.760 --> 00:00:33.200] January 1st. New year, new me.
[00:00:33.200 --> 00:00:36.320] Time to take stock of last year's spending.
[00:00:36.320 --> 00:00:41.200] Help me classify last year's expenses into personal, work, or health.
[00:00:41.200 --> 00:00:43.200] Here are some examples.
[00:00:43.200 --> 00:00:48.560] Now classify all last year expenses.
[00:00:48.560 --> 00:00:56.000] Annual physical health.
[00:00:58.240 --> 00:01:00.080] Perfect. Hairdresser.
[00:01:00.080 --> 00:01:03.280] Coffee with client.
[00:01:03.280 --> 00:01:11.280] Personal. Wait, it said client. That's work.
[00:01:11.280 --> 00:01:14.080] What happened?
[00:01:14.080 --> 00:01:17.680] The floofies wear glasses with many lenses.
[00:01:17.680 --> 00:01:19.680] These are called attention heads.
[00:01:19.680 --> 00:01:22.640] Some lenses track word position.
[00:01:22.640 --> 00:01:24.320] Others track word relationships.
[00:01:25.040 --> 00:01:31.200] When classifying coffee with client, a position-focused lens spotted coffee
[00:01:31.200 --> 00:01:35.200] as the anchor word, the first word in the phrase, and gave it extra weight.
[00:01:35.200 --> 00:01:38.960] A relationship-focused lens matched coffee to Marcus's example.
[00:01:38.960 --> 00:01:42.400] Coffee. Personal. Strong match.
[00:01:42.400 --> 00:01:47.680] Other lenses noticed client and found dinner with client equals work.
[00:01:47.680 --> 00:01:50.560] But client wasn't an anchor position.
[00:01:50.560 --> 00:01:54.320] The stronger pattern won. Confident but wrong.
[00:01:54.320 --> 00:01:57.760] When attention heads compete, position often wins.
[00:01:57.760 --> 00:02:00.880] You can't anticipate every edge case with examples alone.
[00:02:00.880 --> 00:02:02.240] Here's what to do instead.
[00:02:02.240 --> 00:02:03.920] Show contrasting examples.
[00:02:03.920 --> 00:02:06.640] Coffee. Personal. Coffee with client. Work.
[00:02:06.640 --> 00:02:10.240] Add rules. If a transaction mentions client, always classify as work.
[00:02:10.240 --> 00:02:11.600] Define exclusions.
[00:02:11.600 --> 00:02:14.640] The word gym only triggers health when it's an activity.
[00:02:14.640 --> 00:02:18.320] Gym membership equals health. Gym bag equals personal.
[00:02:18.320 --> 00:02:23.040] Without priority rules, attention heads compete and the strongest pattern wins.
[00:02:23.600 --> 00:02:26.080] Often based on word position, not meaning.
[00:02:26.080 --> 00:02:31.360] Explicit rules tell the lenses which words should override everything else.
[00:02:31.920 --> 00:02:35.680] Want AI that handles classification nuance automatically?
[00:02:35.680 --> 00:02:39.760] Visit askwhy.ai and try Prism.
[00:02:39.760 --> 00:02:43.840] So your Coffee with Client always lands in the right category.
[00:02:43.840 --> 00:02:47.760] Happy New Year, AI analysts!